Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for storage and analysis of process data, wherein the process data is generated by executing real processes, wherein the real processes are carried out with the help of one or more IT systems, wherein the process instances of the real processes leave data trails as process data in the IT systems, wherein the method comprises: storing the process data-according to a predetermined data structure as process protocol in a storage means of a computer system, wherein the storage means is coupled to a processor of the computer system operatively, wherein the process data comprise a number of process instances, wherein each process instance comprises a number of process steps, and wherein the predetermined data structure comprises at least: a first attribute, in which a unique identification of the process instance of the respective process step is stored; a second attribute, in which an identification of the respective process step is stored; and a third attribute, in which the sequence of the process steps within a process instance is stored; wherein the method further comprises: receiving, by the computer system, an APE statement (Advanced Process Algebra Execution), wherein the APE statement defines a query of process instances from the storage means, and wherein the APE statement comprises at least one process operator; executing, by the processor, the APE statement and reading the process instances according to the APE statement from the storage means, wherein during reading the process instances from the storage means, processes are reconstructed from the process steps belonging to the process instances; wherein during processing of the process instances according to the process operator, the process operator is applied to the reconstructed processes, wherein during execution of the at least one process operator, a predetermined program code is executed, wherein the program code is provided to the processor or is generated, prior to the execution of the process operator, by the processor, and providing the result of the execute statement for further processing.
The invention relates to a computer-implemented method for storing and analyzing process data generated by real-world processes executed via IT systems. These processes leave data trails in the IT systems, which are captured as process data. The method involves storing this process data in a structured format within a computer system's storage, where the data structure includes attributes for uniquely identifying process instances, identifying individual process steps, and recording the sequence of steps within each instance. The stored process data is then queried using an Advanced Process Algebra Execution (APE) statement, which defines a query for retrieving specific process instances. The APE statement includes at least one process operator, which is executed by the system to reconstruct processes from the stored steps and apply the operator to these reconstructed processes. The operator's execution involves running predefined or dynamically generated program code, with the results made available for further processing. This approach enables efficient storage and analysis of process data, allowing for complex queries and operations on reconstructed process flows.
2. The method of claim 1 , wherein the storage means is a main memory of the computer system.
3. The method of claim 1 , wherein the process steps belonging to a process are sorted according to the third attribute within the data structure and stored in the storage means at adjacent addresses.
4. The method of claim 1 , wherein a process protocol, a table, a value, or a graph are provided as result of the processing.
5. The method of claim 1 , wherein the APE statement comprises a condition indicating according to which output format the result of the query is to be provided for further processing, wherein the output format comprises at least one of a table or a graph.
6. The method of claim 1 , wherein the APE statement comprises nested queries, wherein the queries are executed by the processor sequentially, and wherein the result of a query is provided as a stream of the subsequent query.
7. The method of claim 1 , wherein the at least one process operator comprises at least one of determining a throughput time between two predetermined process steps of the process instances, determining at least one process graph from a number of process instances, determining process instances corresponding to a predetermined pattern, determining frequencies, in particular, frequencies of process steps in processes, determining loops in processes, subtracting process graphs, in particular, determining a difference of two process graphs, extracting sub-processes from processes, splitting process graphs into two or more process graphs, and combinations thereof.
This invention relates to process mining, a field that analyzes and visualizes business processes based on event logs. The technology addresses challenges in understanding and optimizing complex workflows by extracting meaningful insights from process data. The method involves analyzing process instances to derive various metrics and structures, such as throughput times between process steps, process graphs representing workflows, and patterns within processes. It also identifies process instances matching specific patterns, calculates frequencies of process steps, detects loops, and compares process graphs by determining differences. Additionally, the method extracts sub-processes from larger workflows and splits process graphs into multiple segments for deeper analysis. These capabilities enable organizations to identify inefficiencies, bottlenecks, and deviations in their processes, leading to improved decision-making and operational efficiency. The approach leverages event logs to provide a data-driven understanding of real-world process execution, supporting continuous process improvement and compliance monitoring.
8. The method of claim 1 , wherein the APE statement comprises at least one data base operator, wherein the data base operator comprises at least one of aggregate functions, analytical functions, conversion functions, cryptographic functions, date functions, logical functions, mathematical functions, string functions, join operations, and combinations hereof.
9. The method of claim 1 , wherein the executing of the APE statement and the reading of the process instances comprises: providing the APE statement to a compiler; parsing, by the compiler, which is executed by the processor, the APE statement and generating an abstract syntax tree from the APE statement; generating, by the compiler, an execution plan from the abstract syntax tree, wherein the execution plan comprises the at least one process operator; and executing, by the processor, the execution plan, wherein a number of process instances is read from the storage means and is processed according to the at least one process operator.
This invention relates to a method for executing an Abstract Process Execution (APE) statement in a data processing system. The method addresses the challenge of efficiently parsing, compiling, and executing process-oriented statements to handle multiple process instances stored in a database or storage system. The method involves providing an APE statement to a compiler, which parses the statement and generates an abstract syntax tree (AST) representing the structure of the statement. The compiler then converts the AST into an execution plan, which includes one or more process operators defining the operations to be performed on the process instances. The execution plan is executed by a processor, which reads a specified number of process instances from storage and processes them according to the defined operators. This approach optimizes the handling of process instances by leveraging a structured compilation and execution pipeline, ensuring efficient and scalable processing of large datasets. The method supports dynamic generation of execution plans based on the APE statement, allowing for flexible and adaptable process execution in various data processing applications.
10. The method of claim 9 , wherein the data structure comprises a further attribute, in which a reference to external data is stored, wherein the execution plan comprises information on the reference, and wherein during execution of the execution plan, a linking between the process steps and the external data according to the information is triggered, and the external data linked to the respective process step is read and is processed by the processor according to the APE statement.
11. The method of claim 10 , wherein the external data comprises a first relation and at least a second relation, wherein data sets of the first relation can be referenced by the further attribute, and wherein the first relation comprises attributes, in which references to data sets of the at least one second relation are stored.
This invention relates to data processing systems that manage and query relational data structures. The problem addressed is efficiently accessing and correlating data across multiple related tables or datasets in a database, particularly when dealing with complex relationships between entities. The method involves processing external data that includes at least two relations (tables or datasets). The first relation contains attributes that reference data sets from a second relation. Additionally, the first relation includes further attributes that can reference data sets from the second relation or other relations. This structure allows for nested or hierarchical relationships between data, enabling more flexible and efficient querying of interconnected data. The method ensures that when querying the first relation, the system can resolve references to the second relation, allowing for seamless access to related data without requiring separate join operations. This is particularly useful in scenarios where data is distributed across multiple tables or datasets, and relationships between them are dynamic or complex. The approach optimizes performance by reducing the need for repeated joins or complex subqueries, improving both query speed and resource utilization in database systems.
12. The method of claim 9 , wherein during execution of the execution plan, the process instances are read as a stream from the storage means.
13. The method of claim 9 , wherein the APE statement comprises at least one filter, which comprises a number of filter criteria, and which is being executed during the execution of the execution plan, wherein the process instances read from the storage means are filtered according to the filter criteria.
This invention relates to a method for processing data in a system that manages process instances, particularly focusing on filtering process instances during execution. The method addresses the challenge of efficiently retrieving and processing relevant process instances from storage while minimizing unnecessary data access and computational overhead. The system includes a storage means for storing process instances and an execution plan that defines how these instances are processed. A key component is the APE (Application Process Engine) statement, which contains at least one filter. Each filter consists of multiple filter criteria that are applied during the execution of the execution plan. As process instances are read from storage, they are evaluated against the filter criteria, and only those instances that meet the criteria are further processed. This selective filtering reduces the amount of data processed, improving system performance and resource utilization. The method ensures that only relevant process instances are retrieved and processed, optimizing the execution plan's efficiency. The filter criteria can be dynamically adjusted to adapt to different processing requirements, enhancing flexibility. This approach is particularly useful in systems where large volumes of process instances are stored and processed, such as workflow management or business process automation systems.
14. The method of claim 9 , wherein during execution of the execution plan, the at least one process operator is executed at first.
15. The method of claim 14 , wherein the processor after the execution of the at least one process operator, provides at least one reference to the result of the execution for further processing of the result.
16. The method of claim 9 , wherein prior to the generation of the execution plan, the abstract syntax tree is optimized.
A system and method for optimizing the execution of database queries involves generating an execution plan from an abstract syntax tree (AST) representing a database query. The AST is first optimized before the execution plan is generated. Optimization of the AST may include simplifying expressions, eliminating redundant operations, or restructuring the tree to improve efficiency. The execution plan is then generated based on the optimized AST, ensuring that the query is executed in a manner that minimizes computational overhead and maximizes performance. This approach enhances query processing efficiency by reducing unnecessary operations and improving the logical flow of the query execution. The method is particularly useful in database management systems where query performance is critical, such as in large-scale data processing environments. By optimizing the AST before generating the execution plan, the system ensures that the final execution plan is derived from a streamlined and efficient representation of the original query, leading to faster query execution times and reduced resource consumption.
17. The method of claim 14 , wherein after the execution of the at least one process operator, the result of the execution is transmitted to an aggregation automat, which is executed by the processor, wherein the aggregation automat groups the result of the execution using at least one aggregate function, wherein the at least one aggregate function comprises at least one of a number, minimum, maximum, sum, and average value.
This invention relates to data processing systems that execute process operators and aggregate their results. The problem addressed is efficiently organizing and summarizing the outputs of multiple process operations to provide meaningful insights or metrics. The system includes a processor that runs at least one process operator, which performs a specific data transformation or analysis task. After execution, the results are sent to an aggregation automat, also executed by the processor. The aggregation automat applies at least one aggregate function to group and summarize the results. These functions include counting the number of results, finding the minimum or maximum value, calculating the sum, or determining the average. This allows for efficient data summarization and analysis, enabling users to derive key metrics from large datasets. The invention improves data processing workflows by automating the aggregation of results, reducing manual effort, and providing structured outputs for further analysis or reporting. The system is particularly useful in applications requiring real-time data insights, such as monitoring systems, business intelligence tools, or scientific data analysis.
18. The method of claim 1 , wherein the identification of the process step comprises a description of the process step.
19. A computer-based system, comprising: a processor; a storage means being operatively coupled to the processor, wherein process data is stored in the storage means according to a predetermined data structure, wherein the process data comprises a number of process instances, wherein each process instance comprises a number of process steps; a computer readable storage medium being operatively coupled to the processor, wherein instructions are stored on the computer readable storage means, which, if executed by the processor of the system, instruct the processor of the system to execute a method for analysis of process data, which is stored in the storage means of the system according to a predetermined data structure, wherein the process data comprises a number of process instances, wherein each process instance comprises a number of process steps; a complier, which is executed by the processor; wherein the processor is adapted to execute the steps of the method according to claim 1 .
This invention relates to a computer-based system for analyzing process data, addressing the challenge of efficiently processing and interpreting large datasets containing multiple process instances and steps. The system includes a processor, storage means, and a computer-readable storage medium. Process data is stored in the storage means according to a predetermined data structure, where each process instance contains multiple process steps. The storage medium contains instructions that, when executed by the processor, enable the system to analyze the stored process data. A compiler is also executed by the processor to facilitate this analysis. The system is designed to process and interpret the process data, extracting meaningful insights from the structured information. The analysis method involves examining the process instances and their respective steps to identify patterns, anomalies, or other relevant information. The system's architecture ensures efficient data handling and processing, allowing for scalable and accurate analysis of complex process data. This invention is particularly useful in fields requiring detailed process monitoring and optimization, such as manufacturing, logistics, or business workflows.
Unknown
February 9, 2021
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